The automatic recognition of online handwriting is considered from an information theoretic viewpoint. Emphasis is placed on the recognition of unconstrained handwriting, a general combination of cursively written word fragments and discretely written characters. Existing recognition algorithms, such as elastic matching, are severely challenged by the variability inherent in unconstrained handwriting. This motivates the development of a probabilistic framework suitable for the derivation of a fast statistical mixture algorithm. This algorithm exhibits about the same degree of complexity as elastic matching, while being more flexible and potentially more robust. The approach relies on a novel front-end processor that, unlike conventional character or stroke-based processing, articulates around a small elementary unit of handwriting called a frame. The algorithm is based on (1) producing feature vectors representing each frame in one (or several) feature spaces, (2) Gaussian K-means clustering in these spaces, and (3) mixture modeling, taking into account the contributions of all relevant clusters in each space. The approach is illustrated by a simple task involving an 81-character alphabet. Both writer-dependent and writer-independent recognition results are found to be competitive with their elastic matching counterparts.
展开▼
